An Intelligent Rule-Based System For Fault Detection And Diagnosis On A Model-Based Actuator Device

G. Kladis, J. Economou, A. Tsourdos, B. White
{"title":"An Intelligent Rule-Based System For Fault Detection And Diagnosis On A Model-Based Actuator Device","authors":"G. Kladis, J. Economou, A. Tsourdos, B. White","doi":"10.1109/VPPC.2007.4544222","DOIUrl":null,"url":null,"abstract":"Unmanned aerial vehicles due to their large operational potential may be required to travel over long distances and through various weather conditions, which might lead to potential degradation or even failure of their electrical or/and mechanical actuator parts. Control in trajectory derivations and path following processes is highly dependable on these actuators and sensors. Depending on their efficiency, the outcome will be a near optimum solution to every problem. Consequently, the minor failure can degrade the performance of the process and might drive it to an uncontrollable system. Therefore, an efficient mechanism should be capable of making these faults realizable and act accordingly so that a consistent performance actuator performance qualitative or quantitative index is continuously maintained. In this paper electro-mechanical actuator potential failures are firstly detected and then diagnosed for the application of unmanned aerial vehicles. It includes several scenarios of actuator faults and results which demonstrate the fault conditions and the effectiveness of the detection and diagnosis Kalman based algorithms. It involves the diagnosis strategy to minimizing errors produced due to malfunction in components or inaccuracies in the model. The residuals used are generated using empirical actuator models which are chosen under specific operating regimes.","PeriodicalId":345424,"journal":{"name":"2007 IEEE Vehicle Power and Propulsion Conference","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Vehicle Power and Propulsion Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VPPC.2007.4544222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Unmanned aerial vehicles due to their large operational potential may be required to travel over long distances and through various weather conditions, which might lead to potential degradation or even failure of their electrical or/and mechanical actuator parts. Control in trajectory derivations and path following processes is highly dependable on these actuators and sensors. Depending on their efficiency, the outcome will be a near optimum solution to every problem. Consequently, the minor failure can degrade the performance of the process and might drive it to an uncontrollable system. Therefore, an efficient mechanism should be capable of making these faults realizable and act accordingly so that a consistent performance actuator performance qualitative or quantitative index is continuously maintained. In this paper electro-mechanical actuator potential failures are firstly detected and then diagnosed for the application of unmanned aerial vehicles. It includes several scenarios of actuator faults and results which demonstrate the fault conditions and the effectiveness of the detection and diagnosis Kalman based algorithms. It involves the diagnosis strategy to minimizing errors produced due to malfunction in components or inaccuracies in the model. The residuals used are generated using empirical actuator models which are chosen under specific operating regimes.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于模型的执行机构故障检测与诊断的智能规则系统
无人机由于其巨大的操作潜力,可能需要在长距离和各种天气条件下飞行,这可能导致其电气或/和机械执行器部件的潜在退化甚至故障。轨迹推导和路径跟踪过程的控制高度依赖于这些致动器和传感器。取决于它们的效率,结果将是每个问题的近乎最佳的解决方案。因此,小故障会降低流程的性能,并可能导致系统无法控制。因此,一个高效的机构应该能够使这些故障得以实现并采取相应的行动,从而使执行器性能的定性或定量指标持续保持一致的性能。本文针对无人机的应用,首先对机电作动器的潜在故障进行检测和诊断。给出了执行器故障的几种场景和结果,验证了基于卡尔曼的检测诊断算法的故障条件和有效性。它涉及到诊断策略,以尽量减少由于组件故障或模型不准确而产生的错误。使用的残差是使用在特定操作制度下选择的经验致动器模型产生的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Electro Energy Bipolar Wafer Cell Battery Technology for PHEV Applications Ultracapacitor Energy Management and Controller Developments for a Series-Parallel 2-by-2 Hybrid Electric Vehicle Cell Balancing Circuit Implementation with DC/DC Converters Using Super Capacitor Equivalent Circuit Parameters Input Admittance Characteristics of Permanent Magnet Brushless AC Motor Drive Systems Three-Dimensional Energetic Dynamic Model of the Tire-Soil Interaction
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1